# Learning to Transmit with Provable Guarantees in Wireless Federated Learning

arXiv: https://arxiv.org/abs/2304.09329 · HTML: https://mlsanity.com/paper/2304.09329/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 75 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 30 of 31)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Harmonic Networks: Deep Translation and Rotation Equivariance](https://mlsanity.com/paper/1612.04642/) (2016)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Safe Policies for Reinforcement Learning via Primal-Dual Methods](https://mlsanity.com/paper/1911.09101/) (2019)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [Multi-Armed Bandit Based Client Scheduling for Federated Learning](https://mlsanity.com/paper/2007.02315/) (2020)
- [Federated Learning for Internet of Things: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2111.07494/) (2021)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [Distributed Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2011.09430/) (2020)
- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks](https://mlsanity.com/paper/2104.05463/) (2021)
- [Adaptive Contention Window Design using Deep Q-learning](https://mlsanity.com/paper/2011.09418/) (2020)
- [MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks](https://mlsanity.com/paper/2009.13879/) (2020)
- [Quantizing data for distributed learning](https://mlsanity.com/paper/2012.07913/) (2020)
- [Power Allocation for Wireless Federated Learning using Graph Neural Networks](https://mlsanity.com/paper/2111.07480/) (2021)
- [Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization](https://mlsanity.com/paper/1406.3665/) (2014)
- [ML-aided power allocation for Tactical MIMO](https://mlsanity.com/paper/2109.06992/) (2021)
- [On Local Distributions in Graph Signal Processing](https://mlsanity.com/paper/2202.10649/) (2022)
- [Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks](https://mlsanity.com/paper/2201.11799/) (2022)
- [Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets](https://mlsanity.com/paper/2006.09992/) (2020)

## Built on by (most cited first, 3 of 3)

- [Biased Backpressure Routing Using Link Features and Graph Neural Networks](https://mlsanity.com/paper/2407.09753/) (2024)
- [Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival](https://mlsanity.com/paper/2408.05886/) (2024)
- [Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/2608.13961/) (2026)

## Abstract (arXiv)

We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challenging scenarios where the wireless channel is changing during the FL training process and when the training data are not independent and identically distributed (non-i.i.d.) on the local devices. Intuitively, the power policy is designed to optimize the information received at the server end during the FL process under communication constraints. Ultimately, our goal is to improve the accuracy and efficiency of the global FL model being trained. The proposed power allocation policy is parameterized using graph convolutional networks (GCNs), and the associated constrained optimization problem is solved through a primal-dual (PD) algorithm. Theoretically, we show that the formulated problem has a zero duality gap and, once the power policy is parameterized, optimality depends on how expressive this parameterization is. Numerically, we demonstrate that the proposed method outperforms existing baselines under different wireless channel settings and varying degrees of data heterogeneity.